Continuing Education Needs in Electrophysical Agents Among Physiotherapists in Ontario
Bibliographic record
Abstract
Purpose: To determine Ontario physiotherapists’ (PTs) self-identified electrophysical agent (EPA) learning needs, preferred modes of continuing education (CE), and barriers to accessing and integrating CE. Methods: A 17-21 question cross-sectional e-survey was distributed through the Ontario Physiotherapy Association and Ontario universities with physiotherapy programs. The sole inclusion criterion was registration with the College of Physiotherapists of Ontario. Data was collected with REDCap, and the Statistical Package for the Social Sciences was used to calculate frequencies and percentages. Chi-squared or Fisher’s exact tests were used for post hoc analyses. Results: The survey had 123 eligible responses. Respondents identified the most knowledge gaps with newer and specialized EPAs, and across all EPAs the most common knowledge gaps were evidence/efficacy (42%) and parameter selection (35%). Respondents identified cost and time of travel and programming as the largest CE barriers, and showed preferences for online learning. Conclusions: Interest in CE was reported for both newer EPAs (i.e. laser and shockwave) and established modalities (i.e. neuromuscular electrical stimulation and ultrasound). Ontario CE programs should prioritize parameter selection and evidence/efficacy, and be facilitated online. Ontario PTs’ area of practice should be considered as this sample found it influenced CE preferences, whereas graduation year had no influence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".